Greedy Active Learning Algorithm for Logistic Regression Models
Abstract
We study a logistic model-based active learning procedure for binary classification problems, in which we adopt a batch subject selection strategy with a modified sequential experimental design method. Moreover, accompanying the proposed subject selection scheme, we simultaneously conduct a greedy variable selection procedure such that we can update the classification model with all labeled training subjects. The proposed algorithm repeatedly performs both subject and variable selection steps until a prefixed stopping criterion is reached. Our numerical results show that the proposed procedure has competitive performance, with smaller training size and a more compact model, comparing with that of the classifier trained with all variables and a full data set. We also apply the proposed procedure to a well-known wave data set (Breiman et al., 1984) to confirm the performance of our method.
Cite
@article{arxiv.1802.00243,
title = {Greedy Active Learning Algorithm for Logistic Regression Models},
author = {Hsiang-Ling Hsu and Yuan-Chin Ivan Chang and Ray-Bing Chen},
journal= {arXiv preprint arXiv:1802.00243},
year = {2018}
}